Title:  Functions that Apply to Rows and Columns of Matrices (and to Vectors) 

Description:  Highperforming functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vectorbased methods, e.g. binMeans(), madDiff() and weightedMedian(). 
Authors:  Henrik Bengtsson [aut, cre, cph], Constantin AhlmannEltze [ctb], Hector Corrada Bravo [ctb], Robert Gentleman [ctb], Jan Gleixner [ctb], Peter Hickey [ctb], Ola Hossjer [ctb], Harris Jaffee [ctb], Dongcan Jiang [ctb], Peter Langfelder [ctb], Brian Montgomery [ctb], Angelina Panagopoulou [ctb], Hugh Parsonage [ctb], Jakob Peder Pettersen [ctb] 
Maintainer:  Henrik Bengtsson <[email protected]> 
License:  Artistic2.0 
Version:  1.3.0 
Built:  20240710 05:18:43 UTC 
Source:  https://github.com/HenrikBengtsson/matrixStats 
Highperforming functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vectorbased methods, e.g. binMeans(), madDiff() and weightedMedian().
Henrik Bengtsson (2017). matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors). R package version 0.52.2. https://github.com/HenrikBengtsson/matrixStats
Henrik Bengtsson, Hector Corrada Bravo, Robert Gentleman, Ola Hossjer, Harris Jaffee, Dongcan Jiang, Peter Langfelder
Useful links:
Report bugs at https://github.com/HenrikBengtsson/matrixStats/issues
Checks if there are any missing values in an object or not.
Please use base::anyNA()
instead of anyMissing()
,
colAnyNAs()
instead of colAnyMissings()
, and
rowAnyNAs()
instead of rowAnyMissings()
.
anyMissing(x, idxs = NULL, ...) colAnyMissings(x, rows = NULL, cols = NULL, ..., useNames = TRUE) rowAnyMissings(x, rows = NULL, cols = NULL, ..., useNames = TRUE) colAnyNAs(x, rows = NULL, cols = NULL, ..., useNames = TRUE) rowAnyNAs(x, rows = NULL, cols = NULL, ..., useNames = TRUE)
anyMissing(x, idxs = NULL, ...) colAnyMissings(x, rows = NULL, cols = NULL, ..., useNames = TRUE) rowAnyMissings(x, rows = NULL, cols = NULL, ..., useNames = TRUE) colAnyNAs(x, rows = NULL, cols = NULL, ..., useNames = TRUE) rowAnyNAs(x, rows = NULL, cols = NULL, ..., useNames = TRUE)
x 
A 
idxs 
A 
... 
Not used. 
rows 
A 
cols 
A 
useNames 
If 
The implementation of this method is optimized for both speed and memory.
The method will return TRUE
as soon as a missing
value is detected.
Returns TRUE
if a missing value was
detected, otherwise FALSE
.
Henrik Bengtsson
Starting with R v3.1.0, there is anyNA()
in the base,
which provides the same functionality as anyMissing()
.
x < rnorm(n = 1000) x[seq(300, length(x), by = 100)] < NA stopifnot(anyMissing(x) == any(is.na(x)))
x < rnorm(n = 1000) x[seq(300, length(x), by = 100)] < NA stopifnot(anyMissing(x) == any(is.na(x)))
Counts the number of elements in nonoverlapping bins
binCounts(x, idxs = NULL, bx, right = FALSE, ...)
binCounts(x, idxs = NULL, bx, right = FALSE, ...)
x 
A 
idxs 
A 
bx 
A 
right 
If 
... 
Not used. 
binCounts(x, bx, right = TRUE)
gives equivalent results as
rev(binCounts(x, bx = rev(bx), right = FALSE))
, but is faster
and more memory efficient.
Returns an integer
vector
of
length B with nonnegative integers.
Missing values in x
are ignored/dropped. Missing values in bx
are not allowed and gives an error.
Henrik Bengtsson
An alternative for counting occurrences within bins is
hist
, e.g. hist(x, breaks = bx,
plot = FALSE)$counts
. That approach is ~3060% slower than
binCounts(..., right = TRUE)
.
To count occurrences of indices x
(positive
integer
s) in [1, B]
, use tabulate(x,
nbins = B)
, where x
does not have to be sorted first. For
details, see tabulate
().
To average values within bins, see binMeans
().
Computes the sample means in nonoverlapping bins
binMeans(y, x, idxs = NULL, bx, na.rm = TRUE, count = TRUE, right = FALSE, ...)
binMeans(y, x, idxs = NULL, bx, na.rm = TRUE, count = TRUE, right = FALSE, ...)
y 
A 
x 

idxs 
A 
bx 
A 
na.rm 
If 
count 
If 
right 
If 
... 
Not used. 
binMeans(x, bx, right = TRUE)
gives equivalent results as
rev(binMeans(x, bx = sort(bx), right = FALSE))
, but is faster.
Returns a numeric
vector
of
length B.
Data points where either of y
and x
is missing are dropped
(and therefore are also not counted). Nonfinite values in y
are
not allowed and gives an error. Missing values in bx
are not allowed
and gives an error.
Henrik Bengtsson with initial code contributions by Martin Morgan [1].
[1] Rdevel thread Fastest nonoverlapping binning mean
function out there? on Oct 3, 2012
binCounts
(). aggregate
and
mean
().
x < 1:200 mu < double(length(x)) mu[1:50] < 5 mu[101:150] < 5 y < mu + rnorm(length(x)) # Binning bx < c(0, 50, 100, 150, 200) + 0.5 y_s < binMeans(y, x = x, bx = bx) plot(x, y) for (kk in seq_along(y_s)) { lines(bx[c(kk, kk + 1)], y_s[c(kk, kk)], col = "blue", lwd = 2) }
x < 1:200 mu < double(length(x)) mu[1:50] < 5 mu[101:150] < 5 y < mu + rnorm(length(x)) # Binning bx < c(0, 50, 100, 150, 200) + 0.5 y_s < binMeans(y, x = x, bx = bx) plot(x, y) for (kk in seq_along(y_s)) { lines(bx[c(kk, kk + 1)], y_s[c(kk, kk)], col = "blue", lwd = 2) }
Translates matrix indices by rows into indices by columns.
indexByRow(dim, idxs = NULL, ...)
indexByRow(dim, idxs = NULL, ...)
dim 
A 
idxs 
A 
... 
Not used. 
Returns an integer
vector
of
indices.
The current implementation does not support longvector indices,
because both input and output indices are of type integers.
This means that the indices in argument idxs
can only be in
range [1,2^311]. Using a greater value will be coerced to
NA_integer_
. Moreover, returned indices can only be in the
same range [1,2^311].
Henrik Bengtsson
dim < c(5, 4) X < matrix(NA_integer_, nrow = dim[1], ncol = dim[2]) Y < t(X) idxs < seq_along(X) # Assign by columns X[idxs] < idxs print(X) # Assign by rows Y[indexByRow(dim(Y), idxs)] < idxs print(Y) stopifnot(X == t(Y))
dim < c(5, 4) X < matrix(NA_integer_, nrow = dim[1], ncol = dim[2]) Y < t(X) idxs < seq_along(X) # Assign by columns X[idxs] < idxs print(X) # Assign by rows Y[indexByRow(dim(Y), idxs)] < idxs print(Y) stopifnot(X == t(Y))
Accurately computes the logarithm of the sum of exponentials, that is,
$log(sum(exp(lx)))$
. If $lx = log(x)$
, then this is equivalently to
calculating $log(sum(x))$
.
logSumExp(lx, idxs = NULL, na.rm = FALSE, ...)
logSumExp(lx, idxs = NULL, na.rm = FALSE, ...)
lx 

idxs 
A 
na.rm 
If 
... 
Not used. 
This function, which avoid numerical underflow, is often used when computing
the logarithm of the sum of small numbers ($x << 1$
) such as
probabilities.
This is function is more accurate than log(sum(exp(lx)))
when the
values of $x = exp(lx)$
are $x << 1$
. The implementation of this
function is based on the observation that
$log(a + b) = [ la = log(a),
lb = log(b) ] = log( exp(la) + exp(lb) ) = la + log ( 1 + exp(lb  la) )$
Assuming $la > lb$
, then $lb  la < lb$
, and it is less likely
that the computation of $1 + exp(lb  la)$
will not underflow/overflow
numerically. Because of this, the overall result from this function should
be more accurate. Analogously to this, the implementation of this function
finds the maximum value of lx
and subtracts it from the remaining
values in lx
.
Returns a numeric
scalar.
This method is optimized for correctness, that avoiding underflowing. It is implemented in native code that is optimized for speed and memory.
Henrik Bengtsson
[1] R Core Team, Writing R Extensions, v3.0.0, April 2013.
[2] Laurent El Ghaoui, HyperTextbook: Optimization Models
and Applications, University of California at Berkeley, August 2012.
(Chapter 'LogSumExp (LSE) Function and Properties')
[3] Rhelp thread logsumexp function in R, 20110217.
https://stat.ethz.ch/pipermail/rhelp/2011February/269205.html
To compute this function on rows or columns of a matrix, see
rowLogSumExps
().
For adding two double values in native code, R provides the C
function logspace_add()
[1]. For properties of the
logsumexponential function, see [2].
## EXAMPLE #1 lx < c(1000.01, 1000.02) y0 < log(sum(exp(lx))) print(y0) ## Inf y1 < logSumExp(lx) print(y1) ## 1000.708 ## EXAMPLE #2 lx < c(1000.01, 1000.02) y0 < log(sum(exp(lx))) print(y0) ## Inf y1 < logSumExp(lx) print(y1) ## 999.3218 ## EXAMPLE #3 ## Rhelp thread 'Beyond doubleprecision?' on May 9, 2009. set.seed(1) x < runif(50) ## The logarithm of the harmonic mean y0 < log(1 / mean(1 / x)) print(y0) ## 1.600885 lx < log(x) y1 < log(length(x))  logSumExp(lx) print(y1) ## [1] 1.600885 # Sanity check stopifnot(all.equal(y1, y0))
## EXAMPLE #1 lx < c(1000.01, 1000.02) y0 < log(sum(exp(lx))) print(y0) ## Inf y1 < logSumExp(lx) print(y1) ## 1000.708 ## EXAMPLE #2 lx < c(1000.01, 1000.02) y0 < log(sum(exp(lx))) print(y0) ## Inf y1 < logSumExp(lx) print(y1) ## 999.3218 ## EXAMPLE #3 ## Rhelp thread 'Beyond doubleprecision?' on May 9, 2009. set.seed(1) x < runif(50) ## The logarithm of the harmonic mean y0 < log(1 / mean(1 / x)) print(y0) ## 1.600885 lx < log(x) y1 < log(length(x))  logSumExp(lx) print(y1) ## [1] 1.600885 # Sanity check stopifnot(all.equal(y1, y0))
Calculates the product for each row (column) in a matrix.
product(x, idxs = NULL, na.rm = FALSE, ...) rowProds(x, rows = NULL, cols = NULL, na.rm = FALSE, method = c("direct", "expSumLog"), ..., useNames = TRUE) colProds(x, rows = NULL, cols = NULL, na.rm = FALSE, method = c("direct", "expSumLog"), ..., useNames = TRUE)
product(x, idxs = NULL, na.rm = FALSE, ...) rowProds(x, rows = NULL, cols = NULL, na.rm = FALSE, method = c("direct", "expSumLog"), ..., useNames = TRUE) colProds(x, rows = NULL, cols = NULL, na.rm = FALSE, method = c("direct", "expSumLog"), ..., useNames = TRUE)
x 

idxs 
A 
na.rm 
If 
... 
Not used. 
rows 
A 
cols 
A 
method 
A 
useNames 
If 
If method = "expSumLog"
, then then product
() function is
used, which calculates the product via the logarithmic transform (treating
negative values specially). This improves the precision and lowers the risk
for numeric overflow. If method = "direct"
, the direct product is
calculated via the prod
() function.
Returns a numeric
vector
of
length N (K).
Note, if method = "expSumLog"
, na.rm = FALSE
, and x
contains missing values (NA
or
NaN
), then the calculated value is also
missing value. Note that it depends on platform whether
NaN
or NA
is returned
when an NaN
exists, cf.
is.nan
().
Henrik Bengtsson
Checks if a value exists / does not exist in each row (column) of a matrix.
rowAlls(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colAlls(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) allValue(x, idxs = NULL, value = TRUE, na.rm = FALSE, ...) rowAnys(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colAnys(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) anyValue(x, idxs = NULL, value = TRUE, na.rm = FALSE, ...)
rowAlls(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colAlls(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) allValue(x, idxs = NULL, value = TRUE, na.rm = FALSE, ...) rowAnys(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colAnys(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) anyValue(x, idxs = NULL, value = TRUE, na.rm = FALSE, ...)
x 

rows 
A 
cols 
A 
value 
A value to search for. 
na.rm 
If 
dim. 
An 
... 
Not used. 
useNames 
If 
idxs 
A 
These functions takes either a matrix or a vector as input. If a vector,
then argument dim.
must be specified and fulfill prod(dim.) ==
length(x)
. The result will be identical to the results obtained when
passing matrix(x, nrow = dim.[1L], ncol = dim.[2L])
, but avoids
having to temporarily create/allocate a matrix, if only such is needed
only for these calculations.
rowAlls()
(colAlls()
) returns an
logical
vector
of length N (K).
Analogously for rowAnys()
(rowAlls()
).
value
When value
is logical, the result is as if the function is applied
on as.logical(x)
. More specifically, if x
is numeric, then
all zeros are treated as FALSE
, nonzero values as TRUE
,
and all missing values as NA
.
Henrik Bengtsson
rowCounts
x < matrix(FALSE, nrow = 10, ncol = 5) x[3:7, c(2, 4)] < TRUE x[2:4, ] < TRUE x[, 1] < TRUE x[5, ] < FALSE x[, 5] < FALSE print(x) print(rowCounts(x)) # 1 4 4 4 0 3 3 1 1 1 print(colCounts(x)) # 9 5 3 5 0 print(rowAnys(x)) print(which(rowAnys(x))) # 1 2 3 4 6 7 8 9 10 print(colAnys(x)) print(which(colAnys(x))) # 1 2 3 4
x < matrix(FALSE, nrow = 10, ncol = 5) x[3:7, c(2, 4)] < TRUE x[2:4, ] < TRUE x[, 1] < TRUE x[5, ] < FALSE x[, 5] < FALSE print(x) print(rowCounts(x)) # 1 4 4 4 0 3 3 1 1 1 print(colCounts(x)) # 9 5 3 5 0 print(rowAnys(x)) print(which(rowAnys(x))) # 1 2 3 4 6 7 8 9 10 print(colAnys(x)) print(which(colAnys(x))) # 1 2 3 4
Extracts one cell per row (column) from a matrix. The implementation is optimized for memory and speed.
rowCollapse(x, idxs, rows = NULL, dim. = dim(x), ..., useNames = TRUE) colCollapse(x, idxs, cols = NULL, dim. = dim(x), ..., useNames = TRUE)
rowCollapse(x, idxs, rows = NULL, dim. = dim(x), ..., useNames = TRUE) colCollapse(x, idxs, cols = NULL, dim. = dim(x), ..., useNames = TRUE)
x 

idxs 
An index 
rows 
A 
dim. 
An 
... 
Not used. 
useNames 
If 
cols 
A 
Returns a vector
of length N (K).
Henrik Bengtsson
Matrix indexing to index elements in matrices and arrays,
cf. [
().
x < matrix(1:27, ncol = 3) y < rowCollapse(x, 1) stopifnot(identical(y, x[, 1])) y < rowCollapse(x, 2) stopifnot(identical(y, x[, 2])) y < rowCollapse(x, c(1, 1, 1, 1, 1, 3, 3, 3, 3)) stopifnot(identical(y, c(x[1:5, 1], x[6:9, 3]))) y < rowCollapse(x, 1:3) print(y) y_truth < c(x[1, 1], x[2, 2], x[3, 3], x[4, 1], x[5, 2], x[6, 3], x[7, 1], x[8, 2], x[9, 3]) stopifnot(identical(y, y_truth))
x < matrix(1:27, ncol = 3) y < rowCollapse(x, 1) stopifnot(identical(y, x[, 1])) y < rowCollapse(x, 2) stopifnot(identical(y, x[, 2])) y < rowCollapse(x, c(1, 1, 1, 1, 1, 3, 3, 3, 3)) stopifnot(identical(y, c(x[1:5, 1], x[6:9, 3]))) y < rowCollapse(x, 1:3) print(y) y_truth < c(x[1, 1], x[2, 2], x[3, 3], x[4, 1], x[5, 2], x[6, 3], x[7, 1], x[8, 2], x[9, 3]) stopifnot(identical(y, y_truth))
The row and columnwise functions take either a matrix or a vector as
input. If a vector, then argument dim.
must be specified and fulfill
prod(dim.) == length(x)
. The result will be identical to the results
obtained when passing matrix(x, nrow = dim.[1L], ncol = dim.[2L])
,
but avoids having to temporarily create/allocate a matrix, if only such is
needed only for these calculations.
rowCounts(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colCounts(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) count(x, idxs = NULL, value = TRUE, na.rm = FALSE, ...)
rowCounts(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colCounts(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) count(x, idxs = NULL, value = TRUE, na.rm = FALSE, ...)
x 

rows 
A 
cols 
A 
value 
A value to search for. 
na.rm 
If 
dim. 
An 
... 
Not used. 
useNames 
If 
idxs 
A 
rowCounts()
(colCounts()
) returns an
integer
vector
of length N (K).
count()
returns a scalar of type integer
if
the count is less than 2^311 (= .Machine$integer.max
) otherwise
a scalar of type double
.
Henrik Bengtsson
rowAlls
x < matrix(0:11, nrow = 4, ncol = 3) x[2:3, 2:3] < 2:5 x[3, 3] < NA_integer_ print(x) print(rowCounts(x, value = 2)) ## [1] 0 1 NA 0 print(colCounts(x, value = 2)) ## [1] 1 1 NA print(colCounts(x, value = NA_integer_)) ## [1] 0 0 1 print(rowCounts(x, value = 2, na.rm = TRUE)) ## [1] 0 1 1 0 print(colCounts(x, value = 2, na.rm = TRUE)) ## [1] 1 1 0 print(rowAnys(x, value = 2)) ## [1] FALSE TRUE TRUE FALSE print(rowAnys(x, value = NA_integer_)) ## [1] FALSE FALSE TRUE FALSE print(colAnys(x, value = 2)) ## [1] TRUE TRUE NA print(colAnys(x, value = 2, na.rm = TRUE)) ## [1] TRUE TRUE FALSE print(colAlls(x, value = 2)) ## [1] FALSE FALSE FALSE
x < matrix(0:11, nrow = 4, ncol = 3) x[2:3, 2:3] < 2:5 x[3, 3] < NA_integer_ print(x) print(rowCounts(x, value = 2)) ## [1] 0 1 NA 0 print(colCounts(x, value = 2)) ## [1] 1 1 NA print(colCounts(x, value = NA_integer_)) ## [1] 0 0 1 print(rowCounts(x, value = 2, na.rm = TRUE)) ## [1] 0 1 1 0 print(colCounts(x, value = 2, na.rm = TRUE)) ## [1] 1 1 0 print(rowAnys(x, value = 2)) ## [1] FALSE TRUE TRUE FALSE print(rowAnys(x, value = NA_integer_)) ## [1] FALSE FALSE TRUE FALSE print(colAnys(x, value = 2)) ## [1] TRUE TRUE NA print(colAnys(x, value = 2, na.rm = TRUE)) ## [1] TRUE TRUE FALSE print(colAlls(x, value = 2)) ## [1] FALSE FALSE FALSE
Cumulative sums, products, minima and maxima for each row (column) in a matrix.
rowCumsums(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) colCumsums(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) rowCumprods(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) colCumprods(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) rowCummins(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) colCummins(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) rowCummaxs(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) colCummaxs(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE)
rowCumsums(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) colCumsums(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) rowCumprods(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) colCumprods(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) rowCummins(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) colCummins(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) rowCummaxs(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE) colCummaxs(x, rows = NULL, cols = NULL, dim. = dim(x), ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
dim. 
An 
... 
Not used. 
useNames 
If 
Returns a numeric
NxK matrix
of the same mode as x
, except when x
is of mode
logical
, then the return type is
integer
.
Henrik Bengtsson
See cumsum
(), cumprod
(),
cummin
(), and cummax
().
x < matrix(1:12, nrow = 4, ncol = 3) print(x) yr < rowCumsums(x) print(yr) yc < colCumsums(x) print(yc) yr < rowCumprods(x) print(yr) yc < colCumprods(x) print(yc) yr < rowCummaxs(x) print(yr) yc < colCummaxs(x) print(yc) yr < rowCummins(x) print(yr) yc < colCummins(x) print(yc)
x < matrix(1:12, nrow = 4, ncol = 3) print(x) yr < rowCumsums(x) print(yr) yc < colCumsums(x) print(yc) yr < rowCumprods(x) print(yr) yc < colCumprods(x) print(yc) yr < rowCummaxs(x) print(yr) yc < colCummaxs(x) print(yc) yr < rowCummins(x) print(yr) yc < colCummins(x) print(yc)
Calculates difference for each row (column) in a matrix.
rowDiffs(x, rows = NULL, cols = NULL, lag = 1L, differences = 1L, dim. = dim(x), ..., useNames = TRUE) colDiffs(x, rows = NULL, cols = NULL, lag = 1L, differences = 1L, dim. = dim(x), ..., useNames = TRUE)
rowDiffs(x, rows = NULL, cols = NULL, lag = 1L, differences = 1L, dim. = dim(x), ..., useNames = TRUE) colDiffs(x, rows = NULL, cols = NULL, lag = 1L, differences = 1L, dim. = dim(x), ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
lag 
An 
differences 
An 
dim. 
An 
... 
Not used. 
useNames 
If 
Returns a numeric
Nx(K1) or (N1)xK
matrix
.
Henrik Bengtsson
See also diff2
().
x < matrix(1:27, ncol = 3) d1 < rowDiffs(x) print(d1) d2 < t(colDiffs(t(x))) stopifnot(all.equal(d2, d1))
x < matrix(1:27, ncol = 3) d1 < rowDiffs(x) print(d1) d2 < t(colDiffs(t(x))) stopifnot(all.equal(d2, d1))
Estimates of the interquartile range for each row (column) in a matrix.
rowIQRs(x, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colIQRs(x, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) iqr(x, idxs = NULL, na.rm = FALSE, ...)
rowIQRs(x, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colIQRs(x, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) iqr(x, idxs = NULL, na.rm = FALSE, ...)
x 

rows 
A 
cols 
A 
na.rm 
If 
... 
Additional arguments passed to 
useNames 
If 
idxs 
A 
Returns a numeric
vector
of
length N (K).
Contrary to IQR
, which gives
an error if there are missing values and na.rm = FALSE
, iqr()
and its corresponding row and columnspecific functions return
NA
_real_.
Henrik Bengtsson
set.seed(1) x < matrix(rnorm(50 * 40), nrow = 50, ncol = 40) str(x) # Row IQRs q < rowIQRs(x) print(q) q0 < apply(x, MARGIN = 1, FUN = IQR) stopifnot(all.equal(q0, q)) # Column IQRs q < colIQRs(x) print(q) q0 < apply(x, MARGIN = 2, FUN = IQR) stopifnot(all.equal(q0, q))
set.seed(1) x < matrix(rnorm(50 * 40), nrow = 50, ncol = 40) str(x) # Row IQRs q < rowIQRs(x) print(q) q0 < apply(x, MARGIN = 1, FUN = IQR) stopifnot(all.equal(q0, q)) # Column IQRs q < colIQRs(x) print(q) q0 < apply(x, MARGIN = 2, FUN = IQR) stopifnot(all.equal(q0, q))
Accurately computes the logarithm of the sum of exponentials across rows or columns.
rowLogSumExps(lx, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(lx), ..., useNames = TRUE) colLogSumExps(lx, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(lx), ..., useNames = TRUE)
rowLogSumExps(lx, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(lx), ..., useNames = TRUE) colLogSumExps(lx, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(lx), ..., useNames = TRUE)
lx 

rows , cols

A 
na.rm 
If 
dim. 
An 
... 
Not used. 
useNames 
If 
A numeric
vector
of length N
(K).
These methods are implemented in native code and have been optimized for speed and memory.
Native implementation by Henrik Bengtsson. Original R code by Nakayama ??? (Japan).
To calculate the same on vectors, logSumExp
().
Standard deviation estimates for each row (column) in a matrix.
rowMads(x, rows = NULL, cols = NULL, center = NULL, constant = 1.4826, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colMads(x, rows = NULL, cols = NULL, center = NULL, constant = 1.4826, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) rowSds(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, center = NULL, dim. = dim(x), ..., useNames = TRUE) colSds(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, center = NULL, dim. = dim(x), ..., useNames = TRUE)
rowMads(x, rows = NULL, cols = NULL, center = NULL, constant = 1.4826, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colMads(x, rows = NULL, cols = NULL, center = NULL, constant = 1.4826, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) rowSds(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, center = NULL, dim. = dim(x), ..., useNames = TRUE) colSds(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, center = NULL, dim. = dim(x), ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
center 
(optional) The center, defaults to the row means for the SD estimators and row medians for the MAD estimators. 
constant 
A scale factor. See 
na.rm 
If 
dim. 
An 
... 
Additional arguments passed to 
useNames 
If 
refine 
If 
Returns a numeric
vector
of
length N (K).
Henrik Bengtsson
Calculates the mean for each row (column) in a matrix.
rowMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, dim. = dim(x), ..., useNames = TRUE) colMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, dim. = dim(x), ..., useNames = TRUE)
rowMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, dim. = dim(x), ..., useNames = TRUE) colMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, dim. = dim(x), ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
na.rm 
If 
refine 
If 
dim. 
An 
... 
Not used. 
useNames 
If 
The implementation of rowMeans2()
and colMeans2()
is
optimized for both speed and memory.
Returns a numeric
vector
of
length N (K).
Henrik Bengtsson
Calculates the median for each row (column) in a matrix.
rowMedians(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colMedians(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE)
rowMedians(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colMedians(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE)
x 

rows , cols

A 
na.rm 

dim. 
An 
... 
Not used. 
useNames 
If 
The implementation of rowMedians()
and colMedians()
is
optimized for both speed and memory. To avoid coercing to
double
s (and hence memory allocation), there is a
special implementation for integer
matrices. That is,
if x
is an integer
matrix
,
then rowMedians(as.double(x))
(rowMedians(as.double(x))
) would
require three times the memory of rowMedians(x)
(colMedians(x)
), but all this is avoided.
Returns a numeric
vector
of
length N (K).
Henrik Bengtsson, Harris Jaffee
See rowWeightedMedians()
and
colWeightedMedians()
for weighted medians.
For mean estimates, see rowMeans2()
and
rowMeans()
.
Gets an order statistic for each row (column) in a matrix.
rowOrderStats(x, rows = NULL, cols = NULL, which, dim. = dim(x), ..., useNames = TRUE) colOrderStats(x, rows = NULL, cols = NULL, which, dim. = dim(x), ..., useNames = TRUE)
rowOrderStats(x, rows = NULL, cols = NULL, which, dim. = dim(x), ..., useNames = TRUE) colOrderStats(x, rows = NULL, cols = NULL, which, dim. = dim(x), ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
which 
An 
dim. 
An 
... 
Not used. 
useNames 
If 
The implementation of rowOrderStats()
is optimized for both speed and
memory. To avoid coercing to double
s (and hence memory
allocation), there is a unique implementation for
integer
matrices.
Returns a numeric
vector
of
length N (K).
This method does not handle missing values,
that is, the result corresponds to having na.rm = FALSE
(if such an
argument would be available).
The native implementation of rowOrderStats()
was adopted by
Henrik Bengtsson from Robert Gentleman's rowQ()
in the Biobase
package.
See rowMeans()
in colSums
().
Estimates quantiles for each row (column) in a matrix.
rowQuantiles(x, rows = NULL, cols = NULL, probs = seq(from = 0, to = 1, by = 0.25), na.rm = FALSE, type = 7L, digits = 7L, ..., useNames = TRUE, drop = TRUE) colQuantiles(x, rows = NULL, cols = NULL, probs = seq(from = 0, to = 1, by = 0.25), na.rm = FALSE, type = 7L, digits = 7L, ..., useNames = TRUE, drop = TRUE)
rowQuantiles(x, rows = NULL, cols = NULL, probs = seq(from = 0, to = 1, by = 0.25), na.rm = FALSE, type = 7L, digits = 7L, ..., useNames = TRUE, drop = TRUE) colQuantiles(x, rows = NULL, cols = NULL, probs = seq(from = 0, to = 1, by = 0.25), na.rm = FALSE, type = 7L, digits = 7L, ..., useNames = TRUE, drop = TRUE)
x 

rows 
A 
cols 
A 
probs 

na.rm 
If 
type 
An 
digits 
An 
... 
Additional arguments passed to 
useNames 
If 
drop 
If TRUE, singleton dimensions in the result are dropped, otherwise not. 
Returns a NxJ (KxJ) matrix
, where N (K) is the
number of rows (columns) for which the J quantiles are calculated.
The return type is either integer or numeric depending on type
.
Henrik Bengtsson
set.seed(1) x < matrix(rnorm(50 * 40), nrow = 50, ncol = 40) str(x) probs < c(0.25, 0.5, 0.75) # Row quantiles q < rowQuantiles(x, probs = probs) print(q) q_0 < apply(x, MARGIN = 1, FUN = quantile, probs = probs) stopifnot(all.equal(q_0, t(q))) # Column IQRs q < colQuantiles(x, probs = probs) print(q) q_0 < apply(x, MARGIN = 2, FUN = quantile, probs = probs) stopifnot(all.equal(q_0, t(q)))
set.seed(1) x < matrix(rnorm(50 * 40), nrow = 50, ncol = 40) str(x) probs < c(0.25, 0.5, 0.75) # Row quantiles q < rowQuantiles(x, probs = probs) print(q) q_0 < apply(x, MARGIN = 1, FUN = quantile, probs = probs) stopifnot(all.equal(q_0, t(q))) # Column IQRs q < colQuantiles(x, probs = probs) print(q) q_0 < apply(x, MARGIN = 2, FUN = quantile, probs = probs) stopifnot(all.equal(q_0, t(q)))
Gets the range of values in each row (column) of a matrix.
rowRanges(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) rowMins(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) rowMaxs(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colRanges(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colMins(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colMaxs(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE)
rowRanges(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) rowMins(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) rowMaxs(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colRanges(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colMins(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colMaxs(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
na.rm 
If 
dim. 
An 
... 
Not used. 
useNames 
If 
rowRanges()
(colRanges()
) returns a
numeric
Nx2 (Kx2) matrix
, where N
(K) is the number of rows (columns) for which the ranges are calculated.
rowMins()/rowMaxs()
(colMins()/colMaxs()
) returns a
numeric
vector
of length N (K).
Henrik Bengtsson
rowOrderStats
() and pmin.int
().
Gets the rank of the elements in each row (column) of a matrix.
rowRanks(x, rows = NULL, cols = NULL, ties.method = c("max", "average", "first", "last", "random", "max", "min", "dense"), dim. = dim(x), ..., useNames = TRUE) colRanks(x, rows = NULL, cols = NULL, ties.method = c("max", "average", "first", "last", "random", "max", "min", "dense"), dim. = dim(x), preserveShape = FALSE, ..., useNames = TRUE)
rowRanks(x, rows = NULL, cols = NULL, ties.method = c("max", "average", "first", "last", "random", "max", "min", "dense"), dim. = dim(x), ..., useNames = TRUE) colRanks(x, rows = NULL, cols = NULL, ties.method = c("max", "average", "first", "last", "random", "max", "min", "dense"), dim. = dim(x), preserveShape = FALSE, ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
ties.method 
A 
dim. 
An 
... 
Not used. 
useNames 
If 
preserveShape 
A 
These functions rank values and treats missing values the same way as
rank
().
For equal values ("ties"), argument ties.method
determines how these
are ranked among each other. More precisely, for the following values of
ties.method
, each index set of ties consists of:
"first"
 increasing values that are all unique
"last"
 decreasing values that are all unique
"min"
 identical values equaling the minimum of
their original ranks
"max"
 identical values equaling the maximum of
their original ranks
"average"
 identical values that equal the sample mean of
their original ranks. Because the average is calculated, the returned
ranks may be noninteger values
"random"
 randomly shuffled values of their original ranks.
"dense"
 increasing values that are all unique and,
contrary to "first"
, never contain any gaps
For more information on ties.method = "dense"
, see frank()
of
the data.table package.
For more information on the other alternatives, see rank
().
Note that, due to different randomization strategies, the shuffling order
produced by these functions when using ties.method = "random"
does
not reproduce that of rank
().
WARNING: For backwardcompatibility reasons, the default is
ties.method = "max"
, which differs from rank
()
which uses ties.method = "average"
by default.
Since we plan to change the default behavior in a future version, we recommend
to explicitly specify the intended value of argument ties.method
.
A matrix
of type integer
is
returned, unless ties.method = "average"
when it is of type
numeric
.
The rowRanks()
function always returns an NxK
matrix
, where N (K) is the number of rows (columns)
whose ranks are calculated.
The colRanks()
function returns an NxK matrix
, if
preserveShape = TRUE
, otherwise a KxN matrix
.
Any names
of x
are ignored and absent in the
result.
Missing values are ranked as NA_integer_
, as with na.last = "keep"
in the rank
() function.
The implementation is optimized for both speed and memory. To avoid
coercing to double
s (and hence memory allocation),
there is a unique implementation for integer
matrices.
Furthermore, it is more memory efficient to do
colRanks(x, preserveShape = TRUE)
than
t(colRanks(x, preserveShape = FALSE))
.
Hector Corrada Bravo and Harris Jaffee. Peter Langfelder for adding
'ties.method' support. Brian Montgomery for adding more 'ties.method's.
Henrik Bengtsson adapted the original native
implementation of rowRanks()
from Robert Gentleman's rowQ()
in
the Biobase package.
For developers, see also Section Utility functions' in
'Writing R Extensions manual', particularly the
native functions R_qsort_I()
and R_qsort_int_I()
.
Calculates the sum for each row (column) in a matrix.
rowSums2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colSums2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE)
rowSums2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE) colSums2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
na.rm 
If 
dim. 
An 
... 
Not used. 
useNames 
If 
The implementation of rowSums2()
and colSums2()
is
optimized for both speed and memory.
Returns a numeric
vector
of
length N (K).
Henrik Bengtsson
Tabulates the values in a matrix by row (column).
rowTabulates(x, rows = NULL, cols = NULL, values = NULL, ..., useNames = TRUE) colTabulates(x, rows = NULL, cols = NULL, values = NULL, ..., useNames = TRUE)
rowTabulates(x, rows = NULL, cols = NULL, values = NULL, ..., useNames = TRUE) colTabulates(x, rows = NULL, cols = NULL, values = NULL, ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
values 
An 
... 
Not used. 
useNames 
If 
An alternative to these functions, is to use table(x, row(x))
and table(x, col(x))
, with the exception that the latter do not
support the raw
data type.
When there are no missing values in x
, we have that
all(rowTabulates(x) == t(table(x, row(x))))
and
all(colTabulates(x) == t(table(x, col(x))))
.
When there are missing values, we have that
all(rowTabulates(x) == t(table(x, row(x), useNA = "always")[, seq_len(nrow(x))]))
and
all(colTabulates(x) == t(table(x, col(x), useNA = "always")[, seq_len(ncol(x))]))
.
Returns a NxJ (KxJ) matrix
where N (K) is the
number of row (column) vector
s tabulated and J is the
number of values counted.
Henrik Bengtsson
x < matrix(1:5, nrow = 10, ncol = 5) print(x) print(rowTabulates(x)) print(colTabulates(x)) # Count only certain values print(rowTabulates(x, values = 1:3)) y < as.raw(x) dim(y) < dim(x) print(y) print(rowTabulates(y)) print(colTabulates(y))
x < matrix(1:5, nrow = 10, ncol = 5) print(x) print(rowTabulates(x)) print(colTabulates(x)) # Count only certain values print(rowTabulates(x, values = 1:3)) y < as.raw(x) dim(y) < dim(x) print(y) print(rowTabulates(y)) print(colTabulates(y))
Variance estimates for each row (column) in a matrix.
rowVars(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, center = NULL, dim. = dim(x), ..., useNames = TRUE) colVars(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, center = NULL, dim. = dim(x), ..., useNames = TRUE)
rowVars(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, center = NULL, dim. = dim(x), ..., useNames = TRUE) colVars(x, rows = NULL, cols = NULL, na.rm = FALSE, refine = TRUE, center = NULL, dim. = dim(x), ..., useNames = TRUE)
x 

rows 
A 
cols 
A 
na.rm 
If 
refine 
If 
center 
(optional; a vector or length N (K)) If the row (column) means are already estimated, they can be prespecified using this argument. This avoid reestimating them again. _Warning: It is important that a nonbiased sample mean estimate is passed. If not, then the variance estimate of the spread will also be biased._ If NULL (default), the row/column means are estimated internally. 
dim. 
An 
... 
Additional arguments passed to 
useNames 
If 
Returns a numeric
vector
of
length N (K).
The sample variance is estimated as
$n/(n1) * mean((x  center)^2)$
,
where $center$
is estimated as the sample mean, by default.
In matrixStats (< 0.58.0),
$n/(n1) * (mean(x^2)  center^2)$
was used. Both formulas give the same result _when_ 'center' is the sample mean estimate.
Argument 'center' can be used to provide an already existing estimate. It is important that the sample mean estimate is passed. If not, then the variance estimate of the spread will be biased.
For the time being, in order to lower the risk for such mistakes, argument 'center' is occasionally validated against the samplemean estimate. If a discrepancy is detected, an informative error is provided to prevent incorrect variance estimates from being used. For performance reasons, this check is only performed once every 50 times. The frequency can be controlled by R option 'matrixStats.vars.formula.freq', whose default can be set by environment variable 'R_MATRIXSTATS_VARS_FORMULA_FREQ'.
Henrik Bengtsson
See rowMeans()
and rowSums()
in
colSums
().
set.seed(1) x < matrix(rnorm(20), nrow = 5, ncol = 4) print(x) # Row averages print(rowMeans(x)) print(rowMedians(x)) # Column averages print(colMeans(x)) print(colMedians(x)) # Row variabilities print(rowVars(x)) print(rowSds(x)) print(rowMads(x)) print(rowIQRs(x)) # Column variabilities print(rowVars(x)) print(colSds(x)) print(colMads(x)) print(colIQRs(x)) # Row ranges print(rowRanges(x)) print(cbind(rowMins(x), rowMaxs(x))) print(cbind(rowOrderStats(x, which = 1), rowOrderStats(x, which = ncol(x)))) # Column ranges print(colRanges(x)) print(cbind(colMins(x), colMaxs(x))) print(cbind(colOrderStats(x, which = 1), colOrderStats(x, which = nrow(x)))) x < matrix(rnorm(2000), nrow = 50, ncol = 40) # Row standard deviations d < rowDiffs(x) s1 < rowSds(d) / sqrt(2) s2 < rowSds(x) print(summary(s1  s2)) # Column standard deviations d < colDiffs(x) s1 < colSds(d) / sqrt(2) s2 < colSds(x) print(summary(s1  s2))
set.seed(1) x < matrix(rnorm(20), nrow = 5, ncol = 4) print(x) # Row averages print(rowMeans(x)) print(rowMedians(x)) # Column averages print(colMeans(x)) print(colMedians(x)) # Row variabilities print(rowVars(x)) print(rowSds(x)) print(rowMads(x)) print(rowIQRs(x)) # Column variabilities print(rowVars(x)) print(colSds(x)) print(colMads(x)) print(colIQRs(x)) # Row ranges print(rowRanges(x)) print(cbind(rowMins(x), rowMaxs(x))) print(cbind(rowOrderStats(x, which = 1), rowOrderStats(x, which = ncol(x)))) # Column ranges print(colRanges(x)) print(cbind(colMins(x), colMaxs(x))) print(cbind(colOrderStats(x, which = 1), colOrderStats(x, which = nrow(x)))) x < matrix(rnorm(2000), nrow = 50, ncol = 40) # Row standard deviations d < rowDiffs(x) s1 < rowSds(d) / sqrt(2) s2 < rowSds(x) print(summary(s1  s2)) # Column standard deviations d < colDiffs(x) s1 < colSds(d) / sqrt(2) s2 < colSds(x) print(summary(s1  s2))
Calculates the weighted means for each row (column) in a matrix.
rowWeightedMeans(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colWeightedMeans(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE)
rowWeightedMeans(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colWeightedMeans(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE)
x 

w 

rows 
A 
cols 
A 
na.rm 
If 
... 
Not used. 
useNames 
If 
The implementations of these methods are optimized for both speed and
memory. If no weights are given, the corresponding
rowMeans()
/colMeans()
is used.
Returns a numeric
vector
of
length N (K).
Henrik Bengtsson
See rowMeans()
and colMeans()
in
colSums
() for nonweighted means. See also
weighted.mean
.
x < matrix(rnorm(20), nrow = 5, ncol = 4) print(x) # Nonweighted row averages mu_0 < rowMeans(x) mu < rowWeightedMeans(x) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (uniform weights) w < rep(2.5, times = ncol(x)) mu < rowWeightedMeans(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (excluding some columns) w < c(1, 1, 0, 1) mu_0 < rowMeans(x[, (w == 1), drop = FALSE]) mu < rowWeightedMeans(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (excluding some columns) w < c(0, 1, 0, 0) mu_0 < rowMeans(x[, (w == 1), drop = FALSE]) mu < rowWeightedMeans(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted averages by rows and columns w < 1:4 mu_1 < rowWeightedMeans(x, w = w) mu_2 < colWeightedMeans(t(x), w = w) stopifnot(all.equal(mu_2, mu_1))
x < matrix(rnorm(20), nrow = 5, ncol = 4) print(x) # Nonweighted row averages mu_0 < rowMeans(x) mu < rowWeightedMeans(x) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (uniform weights) w < rep(2.5, times = ncol(x)) mu < rowWeightedMeans(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (excluding some columns) w < c(1, 1, 0, 1) mu_0 < rowMeans(x[, (w == 1), drop = FALSE]) mu < rowWeightedMeans(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (excluding some columns) w < c(0, 1, 0, 0) mu_0 < rowMeans(x[, (w == 1), drop = FALSE]) mu < rowWeightedMeans(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted averages by rows and columns w < 1:4 mu_1 < rowWeightedMeans(x, w = w) mu_2 < colWeightedMeans(t(x), w = w) stopifnot(all.equal(mu_2, mu_1))
Calculates the weighted medians for each row (column) in a matrix.
rowWeightedMedians(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colWeightedMedians(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE)
rowWeightedMedians(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colWeightedMedians(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE)
x 

w 

rows 
A 
cols 
A 
na.rm 
If 
... 
Additional arguments passed to 
useNames 
If 
The implementations of these methods are optimized for both speed and
memory. If no weights are given, the corresponding
rowMedians
()/colMedians()
is used.
Returns a numeric
vector
of
length N (K).
Henrik Bengtsson
Internally, weightedMedian
() is used.
See rowMedians
() and colMedians()
for nonweighted
medians.
x < matrix(rnorm(20), nrow = 5, ncol = 4) print(x) # Nonweighted row averages mu_0 < rowMedians(x) mu < rowWeightedMedians(x) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (uniform weights) w < rep(2.5, times = ncol(x)) mu < rowWeightedMedians(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (excluding some columns) w < c(1, 1, 0, 1) mu_0 < rowMedians(x[, (w == 1), drop = FALSE]) mu < rowWeightedMedians(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (excluding some columns) w < c(0, 1, 0, 0) mu_0 < rowMedians(x[, (w == 1), drop = FALSE]) mu < rowWeightedMedians(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted averages by rows and columns w < 1:4 mu_1 < rowWeightedMedians(x, w = w) mu_2 < colWeightedMedians(t(x), w = w) stopifnot(all.equal(mu_2, mu_1))
x < matrix(rnorm(20), nrow = 5, ncol = 4) print(x) # Nonweighted row averages mu_0 < rowMedians(x) mu < rowWeightedMedians(x) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (uniform weights) w < rep(2.5, times = ncol(x)) mu < rowWeightedMedians(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (excluding some columns) w < c(1, 1, 0, 1) mu_0 < rowMedians(x[, (w == 1), drop = FALSE]) mu < rowWeightedMedians(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted row averages (excluding some columns) w < c(0, 1, 0, 0) mu_0 < rowMedians(x[, (w == 1), drop = FALSE]) mu < rowWeightedMedians(x, w = w) stopifnot(all.equal(mu, mu_0)) # Weighted averages by rows and columns w < 1:4 mu_1 < rowWeightedMedians(x, w = w) mu_2 < colWeightedMedians(t(x), w = w) stopifnot(all.equal(mu_2, mu_1))
Estimation of scale based on sequentialorder differences, corresponding to
the scale estimates provided by var
,
sd
, mad
and
IQR
.
varDiff(x, idxs = NULL, na.rm = FALSE, diff = 1L, trim = 0, ...) sdDiff(x, idxs = NULL, na.rm = FALSE, diff = 1L, trim = 0, ...) madDiff(x, idxs = NULL, na.rm = FALSE, diff = 1L, trim = 0, constant = 1.4826, ...) iqrDiff(x, idxs = NULL, na.rm = FALSE, diff = 1L, trim = 0, ...) rowVarDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) colVarDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) rowSdDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) colSdDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) rowMadDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) colMadDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) rowIQRDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) colIQRDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE)
varDiff(x, idxs = NULL, na.rm = FALSE, diff = 1L, trim = 0, ...) sdDiff(x, idxs = NULL, na.rm = FALSE, diff = 1L, trim = 0, ...) madDiff(x, idxs = NULL, na.rm = FALSE, diff = 1L, trim = 0, constant = 1.4826, ...) iqrDiff(x, idxs = NULL, na.rm = FALSE, diff = 1L, trim = 0, ...) rowVarDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) colVarDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) rowSdDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) colSdDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) rowMadDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) colMadDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) rowIQRDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE) colIQRDiffs(x, rows = NULL, cols = NULL, na.rm = FALSE, diff = 1L, trim = 0, ..., useNames = TRUE)
x 

idxs 
A 
na.rm 
If 
diff 
The positional distance of elements for which the difference should be calculated. 
trim 
A 
... 
Not used. 
constant 
A scale factor adjusting for asymptotically normal consistency. 
rows 
A 
cols 
A 
useNames 
If 
Note that norder difference MAD estimates, just like the ordinary MAD
estimate by mad
, apply a correction factor such that
the estimates are consistent with the standard deviation under Gaussian
distributions.
The interquartile range (IQR) estimates does not apply such a
correction factor. If asymptotically normal consistency is wanted, the
correction factor for IQR estimate is 1 / (2 * qnorm(3/4))
, which is
half of that used for MAD estimates, which is 1 / qnorm(3/4)
. This
correction factor needs to be applied manually, i.e. there is no
constant
argument for the IQR functions.
Returns a numeric
vector
of
length 1, length N, or length K.
Henrik Bengtsson
[1] J. von Neumann et al., The mean square successive
difference. Annals of Mathematical Statistics, 1941, 12, 153162.
For the corresponding nondifferentiated estimates, see
var
, sd
, mad
and IQR
. Internally, diff2
() is used
which is a faster version of diff
().
Computes a weighted MAD of a numeric vector.
weightedMad(x, w = NULL, idxs = NULL, na.rm = FALSE, constant = 1.4826, center = NULL, ...) rowWeightedMads(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, constant = 1.4826, center = NULL, ..., useNames = TRUE) colWeightedMads(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, constant = 1.4826, center = NULL, ..., useNames = TRUE)
weightedMad(x, w = NULL, idxs = NULL, na.rm = FALSE, constant = 1.4826, center = NULL, ...) rowWeightedMads(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, constant = 1.4826, center = NULL, ..., useNames = TRUE) colWeightedMads(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, constant = 1.4826, center = NULL, ..., useNames = TRUE)
x 

w 
a vector of weights the same length as 
idxs 
A 
na.rm 
If 
constant 

center 
Optional 
... 
Not used. 
rows 
A 
cols 
A 
useNames 
If 
Returns a numeric
scalar.
Missing values are dropped at the very beginning,
if argument na.rm
is TRUE
, otherwise not.
Henrik Bengtsson
For the nonweighted MAD, see mad
. Internally
weightedMedian
() is used to calculate the weighted median.
x < 1:10 n < length(x) m1 < mad(x) m2 < weightedMad(x) stopifnot(identical(m1, m2)) w < rep(1, times = n) m1 < weightedMad(x, w) stopifnot(identical(m1, m2)) # All weight on the first value w[1] < Inf m < weightedMad(x, w) stopifnot(m == 0) # All weight on the first two values w[1:2] < Inf m1 < mad(x[1:2]) m2 < weightedMad(x, w) stopifnot(identical(m1, m2)) # All weights set to zero w < rep(0, times = n) m < weightedMad(x, w) stopifnot(is.na(m))
x < 1:10 n < length(x) m1 < mad(x) m2 < weightedMad(x) stopifnot(identical(m1, m2)) w < rep(1, times = n) m1 < weightedMad(x, w) stopifnot(identical(m1, m2)) # All weight on the first value w[1] < Inf m < weightedMad(x, w) stopifnot(m == 0) # All weight on the first two values w[1:2] < Inf m1 < mad(x[1:2]) m2 < weightedMad(x, w) stopifnot(identical(m1, m2)) # All weights set to zero w < rep(0, times = n) m < weightedMad(x, w) stopifnot(is.na(m))
Computes the weighted sample mean of a numeric vector.
weightedMean(x, w = NULL, idxs = NULL, na.rm = FALSE, refine = FALSE, ...)
weightedMean(x, w = NULL, idxs = NULL, na.rm = FALSE, refine = FALSE, ...)
x 

w 
a vector of weights the same length as 
idxs 
A 
na.rm 
If 
refine 
If 
... 
Not used. 
Returns a numeric
scalar. If x
is of
zero length, then NaN
is returned, which is consistent with
mean
().
This function handles missing values consistently with
weighted.mean
. More precisely, if na.rm = FALSE
,
then any missing values in either x
or w
will give result
NA_real_
. If na.rm = TRUE
, then all (x, w)
data points
for which x
is missing are skipped. Note that if both x
and
w
are missing for a data points, then it is also skipped (by the same
rule). However, if only w
is missing, then the final results will
always be NA_real_
regardless of na.rm
.
Henrik Bengtsson
mean
() and weighted.mean
.
x < 1:10 n < length(x) w < rep(1, times = n) m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # Pull the mean towards zero w[1] < 5 m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # Put even more weight on the zero w[1] < 8.5 m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # All weight on the first value w[1] < Inf m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # All weight on the last value w[1] < 1 w[n] < Inf m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # All weights set to zero w < rep(0, times = n) m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0))
x < 1:10 n < length(x) w < rep(1, times = n) m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # Pull the mean towards zero w[1] < 5 m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # Put even more weight on the zero w[1] < 8.5 m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # All weight on the first value w[1] < Inf m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # All weight on the last value w[1] < 1 w[n] < Inf m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0)) # All weights set to zero w < rep(0, times = n) m0 < weighted.mean(x, w) m1 < weightedMean(x, w) stopifnot(identical(m1, m0))
Computes a weighted median of a numeric vector.
weightedMedian(x, w = NULL, idxs = NULL, na.rm = FALSE, interpolate = is.null(ties), ties = NULL, ...)
weightedMedian(x, w = NULL, idxs = NULL, na.rm = FALSE, interpolate = is.null(ties), ties = NULL, ...)
x 

w 
a vector of weights the same length as 
idxs 
A 
na.rm 
a logical value indicating whether 
interpolate 
If 
ties 
If 
... 
Not used. 
Returns a numeric
scalar.
For the n
elements x = c(x[1], x[2], ..., x[n])
with positive
weights w = c(w[1], w[2], ..., w[n])
such that sum(w) = S
, the
weighted median is defined as the element x[k]
for which the
total weight of all elements x[i] < x[k]
is less or equal to
S/2
and for which the total weight of all elements x[i] > x[k]
is less or equal to S/2
(c.f. [1]).
When using linear interpolation, the weighted mean of x[k1]
and
x[k]
with weights S[k1]
and S[k]
corresponding to the
cumulative weights of those two elements is used as an estimate.
If w
is missing then all elements of x
are given the same
positive weight. If all weights are zero, NA_real_
is
returned.
If one or more weights are Inf
, it is the same as these weights have
the same weight and the others have zero. This makes things easier for cases
where the weights are result of a division with zero.
If there are missing values in w
that are part of the calculation
(after subsetting and dropping missing values in x
), then the final
result is always NA
of the same type as x
.
The weighted median solves the following optimization problem:
$\alpha^* = \arg_\alpha \min \sum_{i = 1}^{n} w_i x_i\alpha$
where
$x = (x_1, x_2, \ldots, x_n)$
are scalars and
$w = (w_1, w_2, \ldots, w_n)$
are the corresponding "weights" for each
individual $x$
value.
Henrik Bengtsson and Ola Hossjer, Centre for Mathematical Sciences, Lund University. Thanks to Roger Koenker, Econometrics, University of Illinois, for the initial ideas.
[1] T.H. Cormen, C.E. Leiserson, R.L. Rivest, Introduction to Algorithms, The MIT Press, Massachusetts Institute of Technology, 1989.
median
, mean
() and
weightedMean
().
x < 1:10 n < length(x) m1 < median(x) # 5.5 m2 < weightedMedian(x) # 5.5 stopifnot(identical(m1, m2)) w < rep(1, times = n) m1 < weightedMedian(x, w) # 5.5 (default) m2 < weightedMedian(x, ties = "weighted") # 5.5 (default) m3 < weightedMedian(x, ties = "min") # 5 m4 < weightedMedian(x, ties = "max") # 6 stopifnot(identical(m1, m2)) # Pull the median towards zero w[1] < 5 m1 < weightedMedian(x, w) # 3.5 y < c(rep(0, times = w[1]), x[1]) # Only possible for integer weights m2 < median(y) # 3.5 stopifnot(identical(m1, m2)) # Put even more weight on the zero w[1] < 8.5 weightedMedian(x, w) # 2 # All weight on the first value w[1] < Inf weightedMedian(x, w) # 1 # All weight on the last value w[1] < 1 w[n] < Inf weightedMedian(x, w) # 10 # All weights set to zero w < rep(0, times = n) weightedMedian(x, w) # NA # Simple benchmarking bench < function(N = 1e5, K = 10) { x < rnorm(N) gc() t < c() t[1] < system.time(for (k in 1:K) median(x))[3] t[2] < system.time(for (k in 1:K) weightedMedian(x))[3] t < t / t[1] names(t) < c("median", "weightedMedian") t } print(bench(N = 5, K = 100)) print(bench(N = 50, K = 100)) print(bench(N = 200, K = 100)) print(bench(N = 1000, K = 100)) print(bench(N = 10e3, K = 20)) print(bench(N = 100e3, K = 20))
x < 1:10 n < length(x) m1 < median(x) # 5.5 m2 < weightedMedian(x) # 5.5 stopifnot(identical(m1, m2)) w < rep(1, times = n) m1 < weightedMedian(x, w) # 5.5 (default) m2 < weightedMedian(x, ties = "weighted") # 5.5 (default) m3 < weightedMedian(x, ties = "min") # 5 m4 < weightedMedian(x, ties = "max") # 6 stopifnot(identical(m1, m2)) # Pull the median towards zero w[1] < 5 m1 < weightedMedian(x, w) # 3.5 y < c(rep(0, times = w[1]), x[1]) # Only possible for integer weights m2 < median(y) # 3.5 stopifnot(identical(m1, m2)) # Put even more weight on the zero w[1] < 8.5 weightedMedian(x, w) # 2 # All weight on the first value w[1] < Inf weightedMedian(x, w) # 1 # All weight on the last value w[1] < 1 w[n] < Inf weightedMedian(x, w) # 10 # All weights set to zero w < rep(0, times = n) weightedMedian(x, w) # NA # Simple benchmarking bench < function(N = 1e5, K = 10) { x < rnorm(N) gc() t < c() t[1] < system.time(for (k in 1:K) median(x))[3] t[2] < system.time(for (k in 1:K) weightedMedian(x))[3] t < t / t[1] names(t) < c("median", "weightedMedian") t } print(bench(N = 5, K = 100)) print(bench(N = 50, K = 100)) print(bench(N = 200, K = 100)) print(bench(N = 1000, K = 100)) print(bench(N = 10e3, K = 20)) print(bench(N = 100e3, K = 20))
Computes a weighted variance / standard deviation of a numeric vector or across rows or columns of a matrix.
weightedVar(x, w = NULL, idxs = NULL, na.rm = FALSE, center = NULL, ...) weightedSd(...) rowWeightedVars(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colWeightedVars(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) rowWeightedSds(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colWeightedSds(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE)
weightedVar(x, w = NULL, idxs = NULL, na.rm = FALSE, center = NULL, ...) weightedSd(...) rowWeightedVars(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colWeightedVars(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) rowWeightedSds(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE) colWeightedSds(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE)
x 

w 
a vector of weights the same length as 
idxs 
A 
na.rm 
If 
center 
Optional 
... 
Not used. 
rows 
A 
cols 
A 
useNames 
If 
The estimator used here is the same as the one used by the "unbiased"
estimator of the Hmisc package. More specifically,
weightedVar(x, w = w) == Hmisc::wtd.var(x, weights = w)
,
Returns a numeric
scalar.
This function handles missing values consistently with
weightedMean
().
More precisely, if na.rm = FALSE
, then any missing values in either
x
or w
will give result NA_real_
.
If na.rm = TRUE
, then all (x, w)
data points for which
x
is missing are skipped. Note that if both x
and w
are missing for a data points, then it is also skipped (by the same rule).
However, if only w
is missing, then the final results will always
be NA_real_
regardless of na.rm
.
Henrik Bengtsson
For the nonweighted variance, see var
.